Self-Supervised Learning Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Self-Supervised Learning ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Self-Supervised Learning Projects
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Contrastive Transfer Learning for Prediction of Adverse Events in Hospitalized Patients
This project focuses on predicting serious health problems in hospitalized patients before they happen. It uses a computer-generated score called the deterioration index to track patient condition over time. A special learning method helps the computer understand patterns in these scores and make accurate predictions. Hospitals can use this system as an early warning to provide timely care and prevent complications. -
Masked Modeling-Based Ultrasound Image Classification via SelfSupervised Learning
This project uses artificial intelligence to improve how ultrasound images are analyzed. It teaches a computer to understand images without needing human labeling. The system learns by filling in missing parts of images, helping it recognize patterns more effectively. This method makes ultrasound image classification more accurate, even when the images are unclear or noisy. -
Multi-Label Contrastive Learning for Abstract Visual Reasoning
This project teaches computers to solve reasoning puzzles like humans do. Instead of just memorizing patterns, it helps the computer understand the rules behind each puzzle. The system combines deep learning with human-style thinking to solve visual problems more accurately. It performs better than previous methods on major test datasets. -
UKSSL: Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification
This project uses artificial intelligence to analyze medical images. It can learn from a small number of labeled images and many unlabeled ones. The system extracts important features from unlabeled data and improves its accuracy with labeled data. It achieves very high accuracy even with only half the labeled data. -
A Graph-Based Multi-Scale Approach With Knowledge Distillation for WSI Classification
This project is about teaching computers to analyze very large medical images for disease detection. Normally, labeling these images takes too much time. The researchers created a new method that looks at the images at different zoom levels and learns how parts of the image relate to each other. Their approach makes predictions more accurate and works better than previous methods on standard datasets. -
Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection
This project uses machine learning to study how common heart-related lab tests change over time in patients. It first learns general patterns from many patients and then applies this knowledge to detect specific heart problems in smaller patient groups. The method improves prediction accuracy and helps doctors better plan tests and treatments. It shows potential for use in other diseases too. -
UKSSL Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification
This project develops a deep learning system to analyze medical images when only a few labeled examples are available. It first learns useful features from many unlabeled images and then fine-tunes the model using the limited labeled data. The method works well on standard medical image datasets and achieves high accuracy, even better than some models trained with fully labeled data. This approach helps make medical image analysis more efficient when labeling is costly or slow.
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